Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting
Fu, Yuwei; Chai, Hua; Zhen, Zhao; Wang, Fei; Xu, Xunjian; Li, Kangping; Shafie-Khah, Miadreza; Dehghanian, Payman; Catalão, João P. S. (2021-04-08)
Fu, Yuwei
Chai, Hua
Zhen, Zhao
Wang, Fei
Xu, Xunjian
Li, Kangping
Shafie-Khah, Miadreza
Dehghanian, Payman
Catalão, João P. S.
IEEE
08.04.2021
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe202201132280
https://urn.fi/URN:NBN:fi-fe202201132280
Kuvaus
vertaisarvioitu
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©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Tiivistelmä
The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to dramatic fluctuation of PV power generation. Precise cloud distribution information is mainly achieved by ground-based total sky imager, then the future cloud distribution can also be achieved by sky image prediction. In previous studies, traditional digital image processing technology (DIPT) has been widely used in predicting sky images. However, DIPT has two deficiencies: relatively limited input spatiotemporal information and linear extrapolation of images. The first deficiency makes the input spatiotemporal information not rich enough, while the second creates the prediction error from the beginning. To avoid these two deficiencies, convolutional autoencoder (CAE) based sky image prediction models are proposed due to the spatiotemporal feature extraction ability of two-dimensional (2-D) CAEs and 3-D CAEs. For 2-D CAEs and 3-D CAEs, four architectures are given respectively. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry and Fourier phase correlation theory are introduced to build the benchmark models. Besides, five different scenarios are also set and the results show that the proposed models outperform the benchmark models in all scenarios.
Kokoelmat
- Artikkelit [2573]